Python Data Analysis Bootcamp for Beginners: All in One
- Description
- Curriculum
- FAQ
- Reviews
Unlock the power of Python and dive into the dynamic realm of data analysis with our comprehensive bootcamp tailored for beginners. In the “Python Data Analysis Bootcamp for Beginners: All in One,” we guide you through every essential aspect of Python programming and data analysis, equipping you with the skills needed to thrive in today’s data-driven world.
Key Course Highlights:
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Master Python Essentials:
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Lay a solid foundation with a hands-on approach to mastering Python basics.
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Learn the syntax, data types, and control structures to build a strong programming foundation.
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Data Cleaning and Manipulation:
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Explore techniques for cleaning and organizing raw data.
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Gain proficiency in data manipulation using Python libraries, ensuring your data is ready for analysis.
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Data Analysis and Transformation:
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Dive into the core of data analysis, learning how to extract meaningful insights.
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Acquire skills to transform and reshape data to derive actionable conclusions.
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Statistical Analysis:
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Understand fundamental statistical concepts and their application in data analysis.
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Learn how to interpret and draw conclusions from statistical data.
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Hypothesis Testing:
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Master the art of hypothesis testing to make informed decisions based on statistical evidence.
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Apply hypothesis testing techniques to validate assumptions and draw accurate conclusions.
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Real-world Projects and Scenarios:
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Immerse yourself in hands-on projects simulating real-world data challenges.
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Apply your knowledge to practical situations, solidifying your skills through experiential learning.
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Why Choose Our Bootcamp?
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Beginner-Friendly: No prior coding experience? No problem! Our course is designed for beginners, starting from the basics and guiding you step-by-step to becoming a proficient data analyst.
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Comprehensive Curriculum: Covering Python essentials to advanced statistical analysis, our all-in-one curriculum ensures you gain a well-rounded understanding of data analysis.
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Smart Application of ChatGPT: Experience a unique blend of traditional teaching methods and AI assistance. ChatGPT is intelligently applied to explain complex Python coding in simple layman’s terms, enhancing your learning experience.
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Hands-On Guidance: Learn not just the ‘how’ but also the ‘why’ behind each concept with hands-on guidance, empowering you to tackle real-world data challenges confidently.
Embark on a transformative journey where you’ll not only master Python but also emerge as a skilled data analyst. Enroll now in the Python Data Analysis Bootcamp for Beginners: All in One and open doors to a world of possibilities in the field of data analysis. Your data story begins here!
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3Why Python?Video lesson
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4Your First Python Code: Getting StartedVideo lesson
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5Variables and naming conventionsVideo lesson
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6Data types: integers, float, strings, booleanVideo lesson
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7Type conversion and castingVideo lesson
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8Arithmetic operators (+, -, *, /, %, **)Video lesson
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9Comparison operators (>, =, <=, ==, !=)Video lesson
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10Logical operators (and, or, not)Video lesson
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11Python Programming Basics – Level 1Quiz
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12Lists: creation, indexing, slicing, modifyingVideo lesson
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13Sets: unique elements, operationsVideo lesson
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14Dictionaries: key-value pairs, methodsVideo lesson
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15Conditional statements (if, elif, else)Video lesson
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16Logical expressions in conditionsVideo lesson
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17Looping structures (for loops, while loops)Video lesson
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18Defining, Creating and Calling functionsVideo lesson
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19Python Programming Basics – Level 2Quiz
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23Importing dataset into Jupyter NotebookVideo lesson
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24Imputing missing values with SimpleImputerVideo lesson
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25Finding and dealing with inconsistent dataVideo lesson
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26Identify and assign correct datasetVideo lesson
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27Dealing with duplicate valuesVideo lesson
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28Data Cleaning in PythonQuiz
I believe that you have loaded the required practice data in the same directory within a jupyter notebook as commanded in Lecture 4. If not, please ensure that you have successfully completed the instructions given in the lecture.
Then, you may proceed to take the QUIZ.
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29Sorting and arranging datasetVideo lesson
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30Conditional Filtering of datasetVideo lesson
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31Merging extra data with the datasetVideo lesson
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32Concatenating variables within datasetVideo lesson
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33Data ManipulationQuiz
I believe that you have loaded the required practice data in the same directory within a jupyter notebook as commanded in Lecture 4. If not, please ensure that you have successfully completed the instructions given in the lecture. Additionally, you have to complete the QUIZ 3 successfully to complete this QUIZ.
Then, you may proceed to take the QUIZ.
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34What is exploratory data analysis?Video lesson
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35Frequency and percentage analysisVideo lesson
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36Descriptive analysis for numeric dataVideo lesson
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37Grouping analysis - numeric measure by nominal dataVideo lesson
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38Pivot table - a tabulation of insightsVideo lesson
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39Crosstabulation - categorical v/s categorical dataVideo lesson
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40Correlation - numeric v/s numeric dataVideo lesson
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41Exploratory data analysisQuiz
I believe that you have loaded the required practice data in the same directory within a jupyter notebook as commanded in Lecture 4. If not, please ensure that you have successfully completed the instructions given in the lecture. Additionally, you have to complete the QUIZ 3 and QUIZ 4 successfully to complete this QUIZ.
Then, you may proceed to take the QUIZ.
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46Test normality of numeric dataVideo lesson
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47Square root transformation methodVideo lesson
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48Logarithm transformation methodVideo lesson
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49Boxcox transformation methodVideo lesson
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50Yeo-johnson transformation methodVideo lesson
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51Data TransformationQuiz
I believe that you have loaded the required practice data in the same directory within a jupyter notebook as commanded in Lecture 4. If not, please ensure that you have successfully completed the instructions given in the lecture. Additionally, you have to complete the QUIZ 3, QUIZ 4 and QUIZ 5 successfully to complete this QUIZ.
Then, you may proceed to take the QUIZ.
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52One sample T-testVideo lesson
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53Independent sample T-testVideo lesson
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54One way analysis of variance (ANOVA)Video lesson
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55Chi-square test for independenceVideo lesson
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56Pearson correlation analysisVideo lesson
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57Linear regression analysisVideo lesson
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58Hypothesis Testing and AnalysisQuiz
I believe that you have loaded the required practice data in the same directory within a jupyter notebook as commanded in Lecture 5. If not, please ensure that you have successfully completed the instructions given in the lecture. Additionally, you have to complete the QUIZ 3 to 7 successfully to complete this QUIZ.
Then, you may proceed to take the QUIZ.

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